Muse Spark vs Claude Opus 5.5 : dix points d’intelligence et une facture 3,7 fois plus élevée

The comparison between Muse Spark and Claude Opus 5.5 is usually written as a scoreboard, and the scoreboard does have a winner: on Artificial Analysis' Intelligence Index the current Muse Spark — Muse Spark 1.3 at its max reasoning setting — scores 48, while Claude Opus 5.5 at max effort with default fallback scores 58 and sits first on the board. That is the number everyone quotes. The number almost nobody quotes sits on the same page: completing one Intelligence Index task costs $1.60 on Muse Spark 1.3 and $5.98 on O​pus 5.5. Ten index points for roughly 3.7x the money per task, with Muse Spark also running about 28% faster and emitting a third fewer tokens to get there. Neither of those figures is ours — both come from Artificial Analysis' own hosted listings — and which one matters depends entirely on what you are doing, so this piece is about separating the two questions rather than picking a side.

D'abord, ce que le nom du modèle dissimule

Muse Spark is not one model, and the matchup only makes sense once you fix a version. The line runs Muse Spark 1.1 (July 16, 2026), Muse Spark 1.2 (August 5, 2026) and Muse Spark 1.3 (September 2, 2026), all from M​eta Superintelligence Labs, all sharing a 1,048,576-token context window and a text-out modality. M​eta labels 1.3 current. Muse Spark 1.3 also gained its "max" reasoning level on September 4, two days after launch, after M​eta finished additional safety testing — and that matters here because every AI-lab scorecard, M​eta's included, was produced at that setting, while the model most developers actually called for the first two days was capped at xhigh.

Claude Opus 5.5 arrived on September 22, 2026, the first release in A​nthropic's 5.5 family, and it is a simpler object: one GA model, adaptive thinking always on, effort selectable from low to max. A​nthropic's own framing was cost — it says O​pus 5.5 costs 40% less to run than Claude Opus 5, with input and output at $4 and $20 per million tokens and cache reads at $0.20, a 60% cut on cache reads versus its predecessor. Claude Fable 5.1 still sits above it for the hardest reasoning work.

Donc, la description honnête est une comparaison entre un modèle qui ne cesse d’être repointé sous le même nom de famille et un modèle qui a été livré une seule fois dans une configuration figée et n’a pas bougé depuis.

Les nombres indépendants, avec leur astérisque

Artificial Analysis est le seul tiers à disposer de classements publics pour les deux, et il ne les compare pas dans la même catégorie — les modèles à poids ouverts sont classés face à des modèles à poids ouverts de la même classe de taille, les modèles propriétaires face à l’ensemble du domaine. Ces deux-ci sont propriétaires, donc cette distorsion particulière n’entre pas en jeu ; ce qui entre en jeu, c’est qu’aucun des deux classements n’est une réexécution contrôlée du harnais de l’autre.

• Muse Spark 1.3 (max) — Intelligence Index 48, classé 24e sur 217 ; vitesse de sortie de 121,9 jetons/seconde ; 1,25 $ en entrée / 4,25 $ en sortie par million de jetons ; 1,60 $ par tâche de l'index ; 170 M de jetons de sortie générés pendant l'évaluation de l'index.

• Claude Opus 5.5 (max, solution de repli par défaut) — Intelligence Index 58, rang 1 sur 217 ; vitesse de sortie 95,2 tokens/seconde ; 4,00 $ en entrée / 20,00 $ en sortie ; 5,98 $ par tâche d'index ; 260 M de tokens en sortie.

Read those two lists side by side and the shape of the trade is visible without any interpretation. O​pus 5.5 wins intelligence by 10 points and wins it while being more verbose, not less — 260M tokens against 170M — so it is not buying its score with economy. Muse Spark 1.3 is faster, cheaper per token, cheaper per completed task, and less talkative. On the price-per-task line is where the gap is widest: you can run roughly 3.7 Muse Spark 1.3 index tasks for the cost of one O​pus 5.5 index task.

Un point de données indépendant de plus, car il va dans l’autre sens et mérite d’être signalé : le classement textuel de LMArena place une configuration de Claude Opus 5.5 au 3e rang global avec 1508 Elo, et Muse Spark 1.3 n’apparaît pas du tout dans ce classement. Le vote selon les préférences humaines et un indice de référence ne s’accordent pas sur l’ampleur, ce qui est normal, mais cela signifie bien que l’écart de 10 points ne doit pas être interprété comme « 10 % moins bon en tout ».

Là où l’écart est le plus large, selon les fournisseurs

Les tableaux de bord fournisseurs ne sont pas comparables entre eux et aucun des deux fournisseurs n’a publié les mêmes benchmarks, mais le recoupement est instructif. Tout ce qui suit est rapporté par les fournisseurs et n’a pas été reproduit par nous.

• Terminal-Bench 4.0 — O​pus 5.5: 66.4%, run at xhigh effort per A​nthropic's note. M​eta reports Muse Spark on Terminal-Bench 2.1 instead, where the family's published score is 76.4 with the Terminus 2 agent. Different benchmark versions, different agents — do not subtract them.

• GDPval-AA — O​pus 5.5: 1846 Elo on v2.1. Muse Spark 1.3: 1,754 on v2. The version moved between the two measurements, so the 92-point gap is directionally real and numerically soft.

• Agentic computer use — O​pus 5.5: 81.8% on OSWorld 2.1, marked "partial". Muse Spark 1.3: 66.9 on OSWorld 2.0. Again, different revisions of the same benchmark family.

• Terminal-Bench-Science 0.1 — O​pus 5.5: 58.7%. M​eta publishes no equivalent for Muse Spark 1.3.

A​nthropic is unusually explicit about the limits of its own table, noting that at this level of capability "benchmark margins have become a less reliable guide to real-world differences". That sentence is worth taking at face value in both directions: it undercuts the 10-point AA gap as much as it undercuts M​eta's DeepSWE placement claims.

Ce que vous abandonnez en plus des points

• Modalités d'entrée — Muse Spark 1.3 accepte le texte, l'image et la vidéo. Claude Opus 5.5 accepte le texte et l'image. Si votre pipeline injecte directement des enregistrements d'écran ou de la vidéo dans le modèle, le choix est évident et l'écart d'indice n'a aucune importance.

• Maximum output — O​pus 5.5 returns up to 128K tokens synchronously, and 300K through the Batches API behind a beta header. M​eta does not publish a max-output figure for Muse Spark 1.3 that we could verify. If you generate long artifacts in a single call, an unpublished ceiling is a risk you should test rather than assume.

• Cache economics — O​pus 5.5's cache read is $0.20 per million, a 95% discount off input. Muse Spark 1.3's is $0.15, an 88% discount. For a heavily cached agent loop this narrows or inverts the per-task arithmetic versus the headline rates, and it is the single easiest thing to get wrong when comparing these two on list price alone.

• Contexte — les deux sont à 1M. Rien à séparer.

• Release cadence — O​pus 5.5 is one model with one price and has not changed since September. Muse Spark has re-pointed three times in three months and then added a reasoning level on top. That is either healthy iteration or an integration hazard, depending on how much you like re-running evals.

Exécuter les deux sans une seconde intégration

Both sides of this comparison are on OrcaRouter — Claude Opus 5.5 and the two Muse Spark checkpoints we serve, Muse Spark 1.2 and Muse Spark 1.1 — through one API across 200+ models, at each provider's list price with 0% markup passed through. Which is the practical answer to the question this article poses: the way to find out whether your workload really needs the extra ten index points is to route a slice of production traffic to Muse Spark 1.2 at $1.25 in / $4.25 out and compare it against the same prompts on O​pus 5.5, on the same key, with automatic failover so a bad run degrades to the other model rather than to an error page. No second contract, no second SDK, no migration.

Two honest caveats about our own coverage. Muse Spark 1.3 is not on our catalogue today, so the routing comparison above starts from 1.2, which is the checkpoint Artificial Analysis measures one point below Muse Spark Open and which now carries a deprecation notice pointing at 1.3. And this comparison rests on A​nthropic's and M​eta's published figures plus Artificial Analysis' independent listings; we have not re-run either model's benchmarks, and the vendor rows above should be read as claims.

La version qui mérite qu’on y revienne

If the question you brought here is "is the cheap one good enough", the honest answer today is that the cheap one is 48 to O​pus 5.5's 58, faster, less verbose and about 3.7x cheaper per completed task — and that A​nthropic's own caveat about benchmark margins is the strongest argument for testing rather than deciding. If your work involves video in, extremely long single outputs, or the last few points of hard reasoning, O​pus 5.5 is the model that shipped with those things documented. The thing to watch is not the next Muse Spark version number but whether Muse Spark 1.3 arrives on a router you already use, and at what price — the family's pricing has been stable at $1.25 / $4.25 across 1.1, 1.2 and 1.3, which is itself a signal that M​eta is competing on capability per dollar rather than on the dollar.

One development belongs outside this matchup rather than inside it, and it is the reason this family is being written about this week at all. On October 15, 2026 — thirteen days after Muse Spark 1.3 shipped and the day before this article — M​eta published the weights and licence for Muse Spark Open, the family's separate open-weight member, whose repository had been sitting gated and unannounced since September 15. The 816B-total / 42B-active architecture currently circulating is that release, not the closed 1.3 measured above. Artificial Analysis does list it, at an intelligence index of 39 against the 48 this article attributes to Muse Spark 1.3, and M​eta has not published the evaluation report behind its own scorecard for it. So the artefact is confirmed and the performance claim is not; do not read this comparison as covering it.

Ce que les chiffres ne peuvent pas vous dire, c'est si quelque chose a mal tourné sur le terrain : les incidents d'IA documentés et les défaillances d'agents concernant les modèles de cette classe sont recensés dans l'OrcaRouter AI Incident Archive, où chaque entrée comporte ses propres sources, sa gravité, son niveau de confiance et son indicateur de contestation.